library(cartography)
library(sf)
#> Linking to GEOS 3.7.1, GDAL 2.4.0, PROJ 5.2.0
# create an sf object centered on Tizi Ouzou city
tizi <- data.frame(name = "Tizi Ouzou", longitude = 4.06, latitude = 36.71)
tizi <- st_as_sf(tizi, coords = c("longitude", "latitude"), crs = 4326)
tizi <- st_transform(tizi, 3857)
tizi <- st_buffer(tizi, 5000)
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library(cartography) | |
library(sp) | |
# Load data | |
data(nuts2006) | |
# Get a SpatialLinesDataFrame of countries borders | |
nuts0.contig.spdf <- getBorders(nuts0.spdf) | |
# Get the GDP per capita | |
nuts0.df$gdpcap <- nuts0.df$gdppps2008/nuts0.df$pop2008*1000000 |
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# World basemap | |
download.file(url = "https://raw.githubusercontent.com/riatelab/basemaps/master/World/countries.geojson", | |
destfile = "country.geojson") | |
# Graticules layer | |
download.file(url = "https://raw.githubusercontent.com/riatelab/basemaps/master/World/graticule30.geojson", | |
destfile = "graticule.geojson") | |
# Population data base | |
download.file(url = "https://population.un.org/wup/Download/Files/WUP2018-F12-Cities_Over_300K.xls", | |
destfile = "citypop.xls") |
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library(SpatialPosition) | |
library(raster) | |
data(hospital) | |
# Compute Stewart potentials from known points (hospital) on a | |
# grid defined by its resolution | |
pot <- stewart(knownpts = hospital, varname = "capacity", | |
typefct = "exponential", span = 750, beta = 3, | |
resolution = 100, mask = paris) | |
# Create a raster of potentials values | |
ras <- rasterStewart(x = pot) |
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library(cartography) | |
library(sf) | |
mtq <- st_read(system.file("gpkg/mtq.gpkg", | |
package="cartography")) | |
bks <- getBreaks(v = mtq$MED, nclass = 5, | |
method = "quantile") | |
cols <- carto.pal(pal1 = "turquoise.pal", n1 = 5) | |
choroLayer(x = mtq, var = "MED", | |
breaks = bks, col = cols, |
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library(sf) | |
nc = st_read(system.file("shape/nc.shp", package="sf")) | |
nc <- st_transform(nc, 32119) | |
# regular grid 512096 polygons | |
grid <- st_make_grid(x = nc, cellsize = 500) | |
t0 <- Sys.time() | |
plot(st_geometry(nc)) | |
plot(grid, add = TRUE) | |
# end of the second plot |
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library(units) | |
library(sf) | |
library(cartography) | |
# library(osmdata) | |
# | |
# # define a bounding box | |
# q0 <- opq(bbox = c(2.2247, 48.8188, 2.4611, 48.9019)) | |
# | |
# # extract Paris boundaries |
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library(rnaturalearth) | |
library(cartography) | |
library(sf) | |
lakes <- ne_download(scale = 10, type = "lakes", category = c("physical"), | |
destdir = tempdir(), load = TRUE, returnclass = c("sf")) | |
countries <- ne_download(scale = 10, type = "countries", | |
category = c("cultural"), destdir = tempdir(), | |
load = TRUE, returnclass = c("sf")) | |
countries <- st_transform(countries, "ESRI:54017") |
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library(sf) | |
library(elevatr) | |
library(raster) | |
library(tanaka) | |
# a polygon of bangalore | |
bangalore <- st_read("export.geojson") | |
elevation <- get_elev_raster(bangalore, z = 10) | |
bangalore_elevation <- trim(mask(elevation, bangalore)) | |
# inspect min and max values |
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library(sf) | |
library(osrm) | |
library(maptiles) | |
# build a bbox for Paris | |
bb <- st_bbox(c(xmin = 643069, ymin = 6857478, | |
xmax = 661079, ymax = 6867081), | |
crs = 2154) | |
# get map tiles | |
osm <- get_tiles(x = bb, provider = "CartoDB.PositronNoLabels", | |
crop = TRUE, zoom = 13) |